Toward a more in-depth measurement of cultural distance: A re-evaluation of the underlying assumptions
Bibliographic record
Abstract
Some 20 years ago, Shenkar (2001) criticized several of the underlying assumptions of the cultural distance (CD) construct. Despite this, researchers continue to use the same metric which fails to address many of the underlying problems. As a result, CD studies seem to generate results which are often contradictory. Rather than rejecting the distance metaphor, the main objective of this study is to provide a more in-depth measure of CD that addresses the assumptions of linearity, symmetry, equivalence, and discordance. We propose that, while the size of the cultural distance between home and host countries may be relevant for some dimensions, it is incomplete, as it does not account for the distinct characteristics of the cultural dimensions, the direction toward countries with different profiles and the contextual settings of the study. We test our hypotheses on a sample from the Orbis database consisting of foreign subsidiary firms from Latin America, other emerging markets from outside the region, and from developed countries operating in 10 of the largest economies in Latin America. Our dataset includes 4226 firm-year observations and a combination of 168 home and host countries. Latin America provides a suitable context for this study, not only because of the diversity of firms from different contexts operating in the region, but also because the region allows us to investigate the influence of home country history and tradition on firms’ ability to conduct business in different cultural contexts. Our assessment of CD shows in a precise manner that size together with direction might be adequate for describing the effects of some dimensions of CD on firm performance, while for other dimensions, it is clearly a matter of country profile. By combining our metric with different national culture frameworks, future studies would be able to complement and strengthen our findings and conclusions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.069 | 0.156 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.013 | 0.028 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".